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Add New Features

Hint

We recommend reading the Behavioral Features Introduction before reading this page

Hint

You won’t have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process.

New Features for Existing Sensors

You can add new features to any existing sensors (see list below) by adding a new provider in three steps:

  1. Modify the config.yaml file
  2. Create a provider folder, script and function
  3. Implement your features extraction code

As a tutorial, we will add a new provider for PHONE_ACCELEROMETER called VEGA that extracts feature1, feature2, feature3 in Python and that it requires a parameter from the user called MY_PARAMETER.

Existing Sensors

An existing sensor is any of the phone or Fitbit sensors with a configuration entry in config.yaml:

  • Phone Accelerometer
  • Phone Activity Recognition
  • Phone Applications Foreground
  • Phone Battery
  • Phone Bluetooth
  • Phone Calls
  • Phone Conversation
  • Phone Light
  • Phone Locations
  • Phone Messages
  • Phone Screen
  • Phone WiFI Connected
  • Phone WiFI Visible

Modify the config.yaml file

In this step you need to add your provider configuration section under the relevant sensor in config.yaml. See our example for our tutorial’s VEGA provider for PHONE_ACCELEROMETER:

Example configuration for a new accelerometer provider VEGA
PHONE_ACCELEROMETER:
    TABLE: accelerometer
    PROVIDERS:
        RAPIDS:
            COMPUTE: False
            ...

        PANDA:
            COMPUTE: False
            ...
        VEGA:
            COMPUTE: False
            FEATURES: ["feature1", "feature2", "feature3"]
            MY_PARAMTER: a_string
            SRC_FOLDER: "vega"
            SRC_LANGUAGE: "python"
Key                          Description
[COMPUTE] Flag to activate/deactivate your provider
[FEATURES] List of features your provider supports. Your provider code should only return the features on this list
[MY_PARAMTER] An arbitrary parameter that our example provider VEGA needs. This can be a boolean, integer, float, string or an array of any of such types.
[SRC_LANGUAGE] The programming language of your provider script, it can be python or r, in our example python
[SRC_FOLDER] The name of your provider in lower case, in our example vega (this will be the name of your folder in the next step)

Create a provider folder, script and function

In this step you need to add a folder, script and function for your provider.

  1. Create your provider folder under src/feature/DEVICE_SENSOR/YOUR_PROVIDER, in our example src/feature/phone_accelerometer/vega (same as [SRC_FOLDER] in the step above).
  2. Create your provider script inside your provider folder, it can be a Python file called main.py or an R file called main.R.

  3. Add your provider function in your provider script. The name of such function should be [providername]_features, in our example vega_features

    Python function

    def [providername]_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):

    R function

    [providername]_features <- function(sensor_data, day_segment, provider)

Implement your feature extraction code

The provider function that you created in the step above will receive the following parameters:

Parameter                                       Description
sensor_data_files Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the [PIDS] array in config.yaml)
day_segment The label of the day segment that should be processed.
provider The parameters you configured for your provider in config.yaml will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains {MY_PARAMETER:"a_string"}
filter_data_by_segment Python only. A function that you will use to filter your data. In R this function is already available in the environment.
*args Python only. Not used for now
**kwargs Python only. Not used for now

The code to extract your behavioral features should be implemented in your provider function and in general terms it will have three stages:

1. Read a participant’s data by loading the CSV data stored in the file pointed by sensor_data_files
acc_data = pd.read_csv(sensor_data_files["sensor_data"])

Note that phone’s battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on)

2. Filter your data to process only those rows that belong to day_segment

This step is only one line of code, but to undersand why we need it, keep reading. 

acc_data = filter_data_by_segment(acc_data, day_segment)

You should use the filter_data_by_segment() function to process and group those rows that belong to each of the day segments RAPIDS could be configured with.

Let’s understand the filter_data_by_segment() function with an example. A RAPIDS user can extract features on any arbitrary day segment. A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for p01. The labels are arbritrary and the instances depend on the days a participant was monitored for:

  • the daily segment could be named my_days and if p01 was monitored for 14 days, it would have 14 instances
  • the weekly segment could be named my_weeks and if p01 was monitored for 14 days, it would have 2 instances.
  • the weekend segment could be named my_weekends and if p01 was monitored for 14 days, it would have 2 instances.

For this example, RAPIDS will call your provider function three times for p01, once where day_segment is my_days, once where day_segment is my_weeks and once where day_segment is my_weekends. In this example not every row in p01‘s data needs to take part in the feature computation for either segment and the rows need to be grouped differently.

Thus filter_data_by_segment() comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called local_segment. This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our p01‘s my_days, my_weeks, and my_weekends examples). After filtering, you should group the data frame by this column and compute any desired features, for example:

acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()

The reason RAPIDS does not filter the participant’s data set for you is because your code might need to compute something based on a participant’s complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number.

3. Return a data frame with your features

After filtering, grouping your data, and computing your features, your provider function should return a data frame that has:

  • One row per day segment instance (e.g. 14 our p01‘s my_days example)
  • The local_segment column added by filter_data_by_segment()
  • One column per feature. Your feature columns should be named SENSOR_PROVIDER_FEATURE, for example accelerometr_vega_feature1
PHONE_ACCELEROMETER Provider Example

For your reference, this a short example of our own provider (RAPIDS) for PHONE_ACCELEROMETER that computes five acceleration features

def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):

    acc_data = pd.read_csv(sensor_data_files["sensor_data"])
    requested_features = provider["FEATURES"]
    # name of the features this function can compute
    base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
    # the subset of requested features this function can compute
    features_to_compute = list(set(requested_features) & set(base_features_names))

    acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute])
    if not acc_data.empty:
        acc_data = filter_data_by_segment(acc_data, day_segment)

        if not acc_data.empty:
            acc_features = pd.DataFrame()
            # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
            magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1)
            acc_data = acc_data.assign(magnitude = magnitude.values)

            if "maxmagnitude" in features_to_compute:
                acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()
            if "minmagnitude" in features_to_compute:
                acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min()
            if "avgmagnitude" in features_to_compute:
                acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean()
            if "medianmagnitude" in features_to_compute:
                acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median()
            if "stdmagnitude" in features_to_compute:
                acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std()

            acc_features = acc_features.reset_index()

    return acc_features

New Features for Non-Existing Sensors

If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the "Existing Sensors" list above), contact us or request it on Slack and we can add the necessary code so you can follow the instructions above.